The subject matter discussed in this section should not be assumed to be prior art merely as a result of its mention in this section. Similarly, a problem mentioned in this section or associated with the subject matter provided as background should not be assumed to have been previously recognized in the prior art. The subject matter in this section merely represents different approaches, which in and of themselves can also correspond to implementations of the claimed technology.
Organizations are generating and accessing vast amount of data, more than ever before, coming from a multitude of sources: log data, location data, behavioral data, sensor data. This flood of data is not only voluminous but comes in many forms, from unstructured to structured and every variation in between. Hence, organizations have an unprecedented opportunity to learn more about their businesses, markets, and customers from the explosion of data being generated from a wealth of sources—from sensors to apps, software to websites. The need to explore, analyze, and gain insights from this data has never been more pressing. With legacy business intelligence and analytics tools, the underlying technology is based on structured, relational databases. Relational databases lack the agility, speed, and true insights necessary to transform data into value.
A number of emerging solutions in recent years have attempted to address the challenges outlined above. Many of them, however, have continued to rely at least partially on the same architecture and technology approach that have caused the challenges in the first place. For example, one solution that has emerged is the use of columnar or in-memory databases, adopted by BI vendors over the past decade. While they moved the needle forward, they were still hampered by the relational model and its associated limitations.
Therefore, an opportunity arises to enable users to explore data in a fast, efficient, self-service, agile way—without dependency on data scientists, cumbersome data warehouse schemas, and slow, resource-intensive IT infrastructure.